import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
		from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=13 ,  lowerCAmelCase__=64 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__=32 ,  lowerCAmelCase__=5 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=37 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=[1, 16, 4, 4] ,  lowerCAmelCase__=None ,  )	->   Any:

							'''simple docstring'''


							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				image_size
							__lowercase					     =				patch_size
							__lowercase					     =				num_channels
							__lowercase					     =				is_training
							__lowercase					     =				use_labels
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				type_sequence_label_size
							__lowercase					     =				initializer_range
							__lowercase					     =				scope
							__lowercase					     =				backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							__lowercase					     =				(self.image_size // 32) ** 2
							__lowercase					     =				num_patches + 1





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							__lowercase					     =				None
							if self.use_labels:
											__lowercase					     =				ids_tensor([self.batch_size] ,  self.type_sequence_label_size		)

							__lowercase					     =				self.get_config()

							return config, pixel_values, labels





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				{
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  is_decoder=lowerCAmelCase__ ,  initializer_range=self.initializer_range ,  backbone_featmap_shape=self.backbone_featmap_shape ,  backbone_config=lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				ViTHybridModel(config=lowerCAmelCase__		)
							model.to(lowerCAmelCase__		)
							model.eval()
							__lowercase					     =				model(lowerCAmelCase__		)
							self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.type_sequence_label_size
							__lowercase					     =				ViTHybridForImageClassification(lowerCAmelCase__		)
							model.to(lowerCAmelCase__		)
							model.eval()
							__lowercase					     =				model(lowerCAmelCase__ ,  labels=lowerCAmelCase__		)
							self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.prepare_config_and_inputs()
							__lowercase     ,	__lowercase     ,	__lowercase					     =				config_and_inputs
							__lowercase					     =				{'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					List[Any]     					=		(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			__a    :					Optional[int]     					=		(
			    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__a    :					Any     					=		False
			__a    :					Optional[Any]     					=		False
			__a    :					Union[str, Any]     					=		False





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				ViTHybridModelTester(self		)
							__lowercase					     =				ConfigTester(self ,  config_class=lowerCAmelCase__ ,  has_text_modality=lowerCAmelCase__ ,  hidden_size=37		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							pass





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)
											self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)		)
											__lowercase					     =				model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCAmelCase__ ,  nn.Linear		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)
											__lowercase					     =				inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowercase					     =				[*signature.parameters.keys()]

											__lowercase					     =				['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							__lowercase					     =				_config_zero_init(lowerCAmelCase__		)
							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(config=lowerCAmelCase__		)
											# Skip the check for the backbone
											for name, module in model.named_modules():
															if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																			__lowercase					     =				[F"{name}.{key}" for key in module.state_dict().keys()]
																			break

											for name, param in model.named_parameters():
															if param.requires_grad:
																			if name in backbone_params:
																							continue
																			self.assertIn(
																			    ((param.data.mean() * 1E9).round() / 1E9).item() ,  [0.0, 1.0] ,  msg=F"Parameter {name} of model {model_class} seems not properly initialized" ,  )





			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase					     =				ViTHybridModel.from_pretrained(lowerCAmelCase__		)
											self.assertIsNotNone(lowerCAmelCase__		)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




@require_torch
@require_vision
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			@cached_property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
							    if is_vision_available()
							    else None
							)





			@slow
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
							    lowerCAmelCase__		)

							__lowercase					     =				self.default_image_processor
							__lowercase					     =				prepare_img()
							__lowercase					     =				image_processor(images=lowerCAmelCase__ ,  return_tensors='''pt'''		).to(lowerCAmelCase__		)

							# forward pass
							with torch.no_grad():
											__lowercase					     =				model(**lowerCAmelCase__		)

							# verify the logits
							__lowercase					     =				torch.Size((1, 10_00)		)
							self.assertEqual(outputs.logits.shape ,  lowerCAmelCase__		)

							__lowercase					     =				torch.tensor([-1.9090, -0.4993, -0.2389]		).to(lowerCAmelCase__		)

							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  lowerCAmelCase__ ,  atol=1E-4		)		)





			@slow
			@require_accelerate
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''		)
							__lowercase					     =				ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' ,  device_map='''auto'''		)

							__lowercase					     =				prepare_img()

							__lowercase					     =				image_processor(images=lowerCAmelCase__ ,  return_tensors='''pt'''		)
							__lowercase					     =				model(**lowerCAmelCase__		)
							__lowercase					     =				outputs.logits
							# model predicts one of the 1000 ImageNet classes
							__lowercase					     =				logits.argmax(-1		).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx] ,  '''tabby, tabby cat'''		)
def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				len(lowercase				)
				__lowercase					     =				len(lowercase				)
				__lowercase					     =				[[False for _ in range(m + 1				)] for _ in range(n + 1				)]
				__lowercase					     =				True
				for i in range(lowercase				):
								for j in range(m + 1				):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				__lowercase					     =				True
																if a[i].islower():
																				__lowercase					     =				True
				return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()